3 citations · 4 across the 3 of their papers we have counts for
9 papers
Golden ratio primal-dual algorithm with linesearch
Xiaokai Chang, Junfeng Yang, Hongchao Zhang
Golden ratio primal-dual algorithm (GRPDA) is a new variant of the classical Arrow-Hurwicz method for solving structured convex optimization problem, in which the objective functio…
An Inexact Accelerated Stochastic ADMM for Separable Convex Optimization
Jianchao Bai, William W. Hager, Hongchao Zhang
An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM) scheme is developed for solving structured separable convex optimization problems with linea…
On the acceleration of the Barzilai-Borwein method
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
The Barzilai-Borwein (BB) gradient method is efficient for solving large-scale unconstrained problems to the modest accuracy and has a great advantage of being easily extended to s…
On the asymptotic convergence and acceleration of gradient methods
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
We consider the asymptotic behavior of a family of gradient methods, which include the steepest descent and minimal gradient methods as special instances. It is proved that each me…
Gradient methods exploiting spectral properties
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
We propose a new stepsize for the gradient method. It is shown that this new stepsize will converge to the reciprocal of the largest eigenvalue of the Hessian, when Dai-Yang's asym…
Generalized Symmetric ADMM for Separable Convex Optimization
Jianchao Bai, Jicheng Li, Fengmin Xu +1
The Alternating Direction Method of Multipliers (ADMM) has been proved to be effective for solving separable convex optimization subject to linear constraints. In this paper, we pr…